As discussed in Section REF , traditional forecasting-based anomaly detection methods are primarily based on auto-regression-based models such as AutoRegressive Integrated Moving Average (ARIMA) [1]}. With the recent advances in deep learning, LSTM has been used to replace auto-regression models [2]}. This architecture allows modeling short-term as well as long-term temporal dependencies.
Deng et al. [3]} have recently proposed a graph-based deep learning model with an attention mechanism for capturing multivariate correlations.
